PROJECT SUMMARY/ABSTRACT Cerebral palsy (CP) is the most common physical disability of childhood with a prevalence of 2.5 to 3.6 cases per 1000 live births. Inadequate physical activity (PA) and poor fitness are major problems impacting the health and well-being of children with CP. Moreover, low PA may contribute to the development of disabling secondary conditions such as obesity, chronic pain, fatigue, and osteoporosis. Children with CP frequently undergo therapeutic interventions and/or orthopedic surgery to improve their mobility and increase habitual PA. The primary outcome measures used pre-post interventions are typically clinical measures of gross motor function or functional capacity. None of these tests, however, measure PA performance. Accelerometry-based motion sensors are the method of choice for assessing PA in children. Our group has shown that accelerometers provide valid and reliable assessments of ambulatory activity in youth with CP. We have developed and validated CP-specific count thresholds to estimate time spent in sedentary, light-intensity, and moderate-to-vigorous intensity PA. However, there is a knowledge gap on optimal approaches in accelerometer data processing to measure PA in children with CP especially given the misclassification error and because cut-points do not perform well in children with more severe functional limitations. The long-term goal of this project is to improve PA measures in children with CP. The overall objective of this application is to use machine learning in accelerometer data processing to improve PA measures in children with CP. The proposed study will be the first to develop, evaluate, and deploy machine learning algorithms to measure activity type and energy expenditure in children with CP. The specific aims of this project are to: 1) Develop and test machine learning algorithms to predict PA type, walking speed, and energy expenditure in ambulant children and adolescents with CP; 2) compare the accuracy of PA intensity estimates provided by machine learning algorithms to those provided by conventional cut-point methods; and 3) evaluate the performance of the resultant CP prediction models in an independent sample of children with Acquired Brain Injury (ABI). The proposed project is in line with the NIH mission because the resultant prediction models will enable clinicians and rehabilitation professionals to more effectively monitor the PA levels of their patients to improve health and function. Improved objective measures of PA will also enable health researchers to better understand the short-and long-term health benefits of regular PA and impact of PA on adverse health conditions associated with CP.